class_docstr: Hero Series Types
  =================
  In texthero, most functions operate on a Pandas Series as input
  and give a Pandas Series as output. There are currently four
  main types of Series / DataFrames in use, which are supported as classes
  by the library:
  
  1. TextSeries: Every cell is a text, i.e. a string. For example,
  `pd.Series(["test", "test"])` is a valid TextSeries.
  
  2. TokenSeries: Every cell is a list of words/tokens, i.e. a list
  of strings. For example, `pd.Series([["test"], ["token2", "token3"]])` is a valid TokenSeries.
  
  3. VectorSeries: Every cell is a vector representing text, i.e.
  a list of floats. For example, `pd.Series([[1.0, 2.0], [3.0]])` is a valid VectorSeries.
  
  Additionally, some Texthero functions (most that accept
  VectorSeries input) accept a Pandas DataFrame as input that is
  representing a matrix.
  Every cell value is one entry in the matrix.
  An example is
  `pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=["word1", "word2", "word3"])`.
  
  These types of Series are supposed to make using the library
  easier and more intuitive. For example, if you see a
  function head
  ```
  def tfidf(s: TokenSeries) -> DataFrame
  ```
  then you know that the function takes a Pandas Series
  whose cells are lists of strings (tokens) and will
  return a sparse Pandas DataFrame where every subcolumn is one feature
  (in this case one word).
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code: def check_type():
      raise NotImplementedError()
  
file_path: texthero/texthero/_types.py
imports: import functools
  import pandas
  from typing import Tuple
label_desc: Use image node_id 1 for calling the HeroTypes obj's underlying member method code with example usage: obj.check_type() without return types